{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reconstruction Search Evaluation\n",
    "Parse, evaluate and visualize the reconstruction search results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "from pathlib import Path\n",
    "import json\n",
    "import copy\n",
    "import math\n",
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.font_manager as font_manager\n",
    "from matplotlib.offsetbox import OffsetImage, AnnotationBbox\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "# Default plot size\n",
    "plt.rcParams[\"figure.figsize\"] = [30,15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def plot_perfect_reconstructions(full_recon_xs, full_recon_ys, labels, title, regraph_limit=None, color=None):\n",
    "    fig, ax = plt.subplots()\n",
    "    # Manually set this to the height of the test set\n",
    "    # ax.set_ylim([0.5, 0.7])\n",
    "    ax.set_ylim([-0.02, 1.02])\n",
    "    for index, x in enumerate(full_recon_xs):\n",
    "        linestyle = \"-\"\n",
    "        if labels is not None:\n",
    "            if labels[index] == \"Ground Truth\":\n",
    "                linestyle = \"--\"\n",
    "            ax.plot(x, full_recon_ys[index], label=labels[index], color=color[index], linewidth=4, linestyle=linestyle)\n",
    "        else:\n",
    "            ax.plot(x, full_recon_ys[index], linewidth=4, color=color[index])\n",
    "    if regraph_limit is not None:\n",
    "        ax.axhline(y=regraph_limit, color='dimgray', linestyle='--')\n",
    "        ax.text(90, regraph_limit - 0.025,'Estimated Representation Upper Limit')\n",
    "\n",
    "    ax.set_xlabel('Steps')\n",
    "    ax.set_ylabel('Exact Reconstruction %')\n",
    "    if title is not None:\n",
    "        ax.set_title(title)\n",
    "    if labels is not None:\n",
    "        ax.legend(loc=\"lower right\", prop={\"size\":20})\n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    "def plot_iou_over_time(iou_over_time, labels=None, title=None, color=None):\n",
    "    fig, ax = plt.subplots()\n",
    "    # ax.set_ylim([0.7, 0.95])\n",
    "    ax.set_ylim([-0.02, 1.02])\n",
    "    for index, iot in enumerate(iou_over_time):\n",
    "        if labels is not None:\n",
    "            ax.plot(range(1, len(iot) + 1), iot, label=labels[index], color=color[index], linewidth=4)\n",
    "        else:\n",
    "            ax.plot(range(1, len(iot) + 1), iot, linewidth=4, color=color[index])\n",
    "    ax.set_xlabel('Step')\n",
    "    ax.set_ylabel('IoU')\n",
    "    if title is not None:\n",
    "        ax.set_title(title)\n",
    "    if labels is not None:\n",
    "        ax.legend(loc=\"lower right\", prop={\"size\":20})\n",
    "    plt.show()\n",
    "\n",
    "def plot_bargraph(labels, values, title=None, color=None, ylim=None):\n",
    "    fig, ax = plt.subplots()\n",
    "    if ylim is not None:\n",
    "        ax.set_ylim(ylim)\n",
    "    ax.bar(labels, values, color=color)\n",
    "    if title is not None:\n",
    "        ax.set_title(title)\n",
    "    plt.show()\n",
    "    \n",
    "def evaluate_log_file(log_file, budget):\n",
    "    \"\"\"Evaluate a single log file\"\"\"\n",
    "    with open(log_file, \"r\", encoding=\"utf8\") as f:\n",
    "        log_data = json.load(f)\n",
    "    max_ious = []\n",
    "    perfect_reconstruction = 0\n",
    "    best_rollout_attempt = -1\n",
    "    best_rollout_attempt_index = -1\n",
    "    current_iou = 0\n",
    "    used_budget = 0\n",
    "    \n",
    "    for index, action in enumerate(log_data):\n",
    "        # Limit to the budget length\n",
    "        if action[\"used_budget\"] < budget:\n",
    "            used_budget = action[\"used_budget\"]\n",
    "            if action[\"max_iou\"] > current_iou:\n",
    "                current_iou = action[\"max_iou\"]\n",
    "                if \"rollout_attempt\" in action:\n",
    "                    best_rollout_attempt = action[\"rollout_attempt\"]\n",
    "                best_rollout_attempt_index = index\n",
    "            max_ious.append(action[\"max_iou\"])\n",
    "            if perfect_reconstruction == 0:\n",
    "                if math.isclose(action[\"max_iou\"], 1, abs_tol=0.00001):\n",
    "                    perfect_reconstruction = action[\"used_budget\"] + 1\n",
    "\n",
    "    \n",
    "    best_rollout_length = 0\n",
    "\n",
    "    # If we didn't increase the max_iou above 0\n",
    "    # then we set to the full budget\n",
    "    if best_rollout_attempt_index < 0:\n",
    "        best_rollout_length = budget\n",
    "    # Beam and best search create a prefix which we real the length of directly\n",
    "    elif \"prefix\" in log_data[best_rollout_attempt_index]:\n",
    "        best_rollout_length = len(log_data[best_rollout_attempt_index][\"prefix\"])\n",
    "    else:\n",
    "        max_set = False\n",
    "        count = False\n",
    "        max_seen = 0\n",
    "        best_rollout_length = 1\n",
    "        # Otherwise loop over again to count the rollout steps taken in the best rollout\n",
    "        for action in reversed(log_data):\n",
    "            if action[\"rollout_attempt\"] == best_rollout_attempt:\n",
    "                if not max_set:\n",
    "                    # Set the max for this roll out attempt\n",
    "                    max_seen = action[\"max_iou\"]\n",
    "                    max_set = True\n",
    "                # When the max drops below max ever seen, start counting\n",
    "                if action[\"max_iou\"] < max_seen:\n",
    "                    count = True\n",
    "                if count:\n",
    "                    best_rollout_length += 1\n",
    "   \n",
    "    time_taken = log_data[-1][\"time\"] - log_data[0][\"time\"]        \n",
    "    return np.array(max_ious), time_taken, perfect_reconstruction, best_rollout_length, used_budget\n",
    "\n",
    "def get_design_extrude_count(file):\n",
    "    \"\"\"Get the number of extrude steps in a design\"\"\"\n",
    "    with open(file, encoding=\"utf8\") as f:\n",
    "        json_data = json.load(f)\n",
    "    timeline = json_data[\"timeline\"]\n",
    "    entities = json_data[\"entities\"]\n",
    "    extrude_count = 0\n",
    "    for timeline_object in timeline:\n",
    "        entity_uuid = timeline_object[\"entity\"]\n",
    "        entity_index = timeline_object[\"index\"]\n",
    "        entity = entities[entity_uuid]\n",
    "        if entity[\"type\"] == \"ExtrudeFeature\":\n",
    "            extrude_count += 1\n",
    "    return extrude_count\n",
    "\n",
    "def get_design_extrude_counts(dataset_dir, split_file):\n",
    "    assert split_file.exists()\n",
    "    with open(split_file, \"r\", encoding=\"utf8\") as f:\n",
    "        split_data = json.load(f)\n",
    "    counts = []\n",
    "    for index, test_file in enumerate(split_data[\"test\"]):\n",
    "        dataset_json_file = dataset_dir / f\"{test_file}.json\"\n",
    "        extrude_count = get_design_extrude_count(dataset_json_file)\n",
    "        counts.append(extrude_count)\n",
    "    return counts\n",
    "\n",
    "def get_budget(log_file):\n",
    "    \"\"\"Get the budget from a log file\"\"\"\n",
    "    with open(log_file, \"r\", encoding=\"utf8\") as f:\n",
    "        log_data = json.load(f)\n",
    "    return log_data[0][\"budget\"]\n",
    "\n",
    "\n",
    "def set_array_length(array, length):\n",
    "    if len(array) > length:\n",
    "        return array[0:length]\n",
    "    elif len(array) < length:\n",
    "        last_value = array[-1]\n",
    "        num_to_add = length - len(array)\n",
    "        values_to_add = np.full(num_to_add, last_value)\n",
    "        return np.append(array, values_to_add)\n",
    "    else:\n",
    "        return array\n",
    "\n",
    "def evaluate(log_dir, dataset_dir, split_file):\n",
    "    \"\"\"Evaluate an experiments logs\"\"\"\n",
    "    log_files = [f for f in log_dir.glob(\"**/*_log.json\")]\n",
    "    log_file_map = {}\n",
    "    for log_file in log_files:\n",
    "        log_file_map[log_file.stem[:-4]] = log_file\n",
    "    \n",
    "    results_file = log_dir / \"search_results.json\"\n",
    "    assert results_file.exists()\n",
    "    with open(results_file, \"r\", encoding=\"utf8\") as f:\n",
    "        results_data = json.load(f)\n",
    "    \n",
    "    assert split_file.exists()\n",
    "    with open(split_file, \"r\", encoding=\"utf8\") as f:\n",
    "        split_data = json.load(f)\n",
    "    \n",
    "\n",
    "    budget = get_budget(log_files[0])\n",
    "    budgets_used = []\n",
    "    times_taken = []\n",
    "    max_iou = []\n",
    "    max_iou_over_time = np.zeros(shape=(len(split_data[\"test\"]), budget))\n",
    "    perfect_reconstructions = []\n",
    "    perfect_reconstruction_files = {}\n",
    "    concise_ratios = []\n",
    "    conciseness = {}\n",
    "    max_extrude_count = 0\n",
    "\n",
    "    result = {\n",
    "        \"log_count\": 0,\n",
    "        \"files_processed\": 0,\n",
    "    }\n",
    "    qual_result = {}\n",
    "    \n",
    "    for index, test_file in enumerate(split_data[\"test\"]):\n",
    "        if test_file in results_data:\n",
    "            result[\"files_processed\"] += 1\n",
    "            processing_result = results_data[test_file]\n",
    "            status = f\"files_{processing_result['status'].lower()}\"\n",
    "            if status not in result:\n",
    "                result[status] = 0\n",
    "            result[status] += 1\n",
    "        if test_file not in log_file_map and test_file not in results_data:\n",
    "            print(f\"File does not have a log or processing record! {test_file}\")\n",
    "        \n",
    "        # Set some default values\n",
    "        max_ious_raw = np.zeros(budget)\n",
    "        perfect_reconstruction = 0\n",
    "        best_rollout_length = 100\n",
    "        used_budget = 0        \n",
    "        \n",
    "        if test_file in log_file_map:\n",
    "            result[\"log_count\"] += 1     \n",
    "            log_file = log_file_map[test_file]\n",
    "            max_ious_raw, time_taken, perfect_reconstruction, best_rollout_length, used_budget = evaluate_log_file(log_file, budget)\n",
    "\n",
    "       \n",
    "        dataset_json_file = dataset_dir / f\"{test_file}.json\"\n",
    "        extrude_count = get_design_extrude_count(dataset_json_file)\n",
    "        if extrude_count > max_extrude_count:\n",
    "            max_extrude_count = extrude_count\n",
    "        concise_ratio = None\n",
    "        if perfect_reconstruction > 0:\n",
    "            concise_ratio =  best_rollout_length / extrude_count\n",
    "            concise_ratios.append(concise_ratio)\n",
    "            conciseness[test_file] = concise_ratio\n",
    "            perfect_reconstruction_files[test_file] = perfect_reconstruction\n",
    "        if test_file in log_file_map:\n",
    "            times_taken.append(time_taken)\n",
    "        budgets_used.append(used_budget)\n",
    "        max_ious = set_array_length(max_ious_raw, budget)\n",
    "        assert len(max_ious) == budget\n",
    "        max_iou_max = np.max(max_ious)\n",
    "        perfect_reconstructions.append(perfect_reconstruction)\n",
    "        max_iou.append(max_iou_max)\n",
    "        max_iou_over_time[index] = max_ious\n",
    "        \n",
    "        qual_result[test_file] = {\n",
    "            \"extrude_count\": extrude_count,\n",
    "            \"best_rollout_length\": best_rollout_length,\n",
    "            \"max_iou\": max_iou_max,\n",
    "            \"concise_ratio\": concise_ratio,\n",
    "            \"perfect_reconstruction\": perfect_reconstruction\n",
    "        }\n",
    "       \n",
    "    result[\"test_file_count\"] = len(split_data[\"test\"])\n",
    "    result[\"time_taken_mean\"] = np.mean(times_taken)\n",
    "    result[\"budget_used_mean\"] = np.mean(budgets_used)\n",
    "    result[\"conciseness\"] = conciseness\n",
    "    result[\"conciseness_mean\"] = np.exp(np.mean(np.log(concise_ratios)))\n",
    "    result[\"perfect_reconstruction\"] = np.count_nonzero(perfect_reconstructions)\n",
    "    result[\"perfect_reconstructions\"] = np.array(perfect_reconstructions)\n",
    "    result[\"perfect_reconstruction_files\"] = perfect_reconstruction_files\n",
    "    result[\"perfect_reconstruction_percent\"] = result[\"perfect_reconstruction\"] / len(split_data[\"test\"])\n",
    "    result[\"max_iou_mean\"] = np.mean(max_iou)\n",
    "    result[\"max_iou_over_time\"] = max_iou_over_time\n",
    "    result[\"max_extrude_count\"] = max_extrude_count\n",
    " \n",
    "    return result, qual_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate the results from each experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_dir = Path(\"/Users/willisk/Autodesk/FusionGallery/Data/Reconstruction/d7/d7\")\n",
    "split_file = dataset_dir.parent / \"train_test.json\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                                                              gcn  \\\n",
       "log_count                                                                    1697   \n",
       "files_processed                                                              1725   \n",
       "files_success                                                                1688   \n",
       "files_fail                                                                     17   \n",
       "files_crash                                                                    15   \n",
       "files_timeout                                                                   5   \n",
       "test_file_count                                                              1725   \n",
       "time_taken_mean                                                         19.486288   \n",
       "budget_used_mean                                                        33.373913   \n",
       "conciseness                     {'41026_295d1dc8_0003': 1.0, '41234_74275eb0_0...   \n",
       "conciseness_mean                                                           1.0168   \n",
       "perfect_reconstruction                                                       1165   \n",
       "perfect_reconstructions         [0, 1, 56, 0, 0, 1, 1, 1, 0, 2, 2, 0, 0, 0, 1,...   \n",
       "perfect_reconstruction_files    {'41026_295d1dc8_0003': 1, '41234_74275eb0_000...   \n",
       "perfect_reconstruction_percent                                           0.675362   \n",
       "max_iou_mean                                                             0.904203   \n",
       "max_iou_over_time               [[0.8846035521300826, 0.8846035521300826, 0.88...   \n",
       "max_extrude_count                                                              20   \n",
       "\n",
       "                                                                              mlp  \\\n",
       "log_count                                                                    1692   \n",
       "files_processed                                                              1725   \n",
       "files_success                                                                1687   \n",
       "files_fail                                                                     17   \n",
       "files_crash                                                                    17   \n",
       "files_timeout                                                                   4   \n",
       "test_file_count                                                              1725   \n",
       "time_taken_mean                                                          19.06079   \n",
       "budget_used_mean                                                        39.648696   \n",
       "conciseness                     {'41026_295d1dc8_0003': 1.0, '41234_74275eb0_0...   \n",
       "conciseness_mean                                                         0.976339   \n",
       "perfect_reconstruction                                                       1029   \n",
       "perfect_reconstructions         [0, 1, 10, 0, 0, 1, 1, 1, 0, 2, 0, 0, 0, 0, 1,...   \n",
       "perfect_reconstruction_files    {'41026_295d1dc8_0003': 1, '41234_74275eb0_000...   \n",
       "perfect_reconstruction_percent                                           0.596522   \n",
       "max_iou_mean                                                              0.85961   \n",
       "max_iou_over_time               [[0.0, 0.8846035521300826, 0.8846035521300826,...   \n",
       "max_extrude_count                                                              20   \n",
       "\n",
       "                                                                              gat  \\\n",
       "log_count                                                                    1708   \n",
       "files_processed                                                              1725   \n",
       "files_success                                                                1702   \n",
       "files_fail                                                                     17   \n",
       "files_crash                                                                   NaN   \n",
       "files_timeout                                                                   6   \n",
       "test_file_count                                                              1725   \n",
       "time_taken_mean                                                         23.172136   \n",
       "budget_used_mean                                                        34.371014   \n",
       "conciseness                     {'41026_295d1dc8_0003': 1.0, '41234_74275eb0_0...   \n",
       "conciseness_mean                                                         1.020557   \n",
       "perfect_reconstruction                                                       1163   \n",
       "perfect_reconstructions         [0, 1, 2, 37, 0, 1, 1, 1, 0, 0, 0, 0, 0, 24, 1...   \n",
       "perfect_reconstruction_files    {'41026_295d1dc8_0003': 1, '41234_74275eb0_000...   \n",
       "perfect_reconstruction_percent                                           0.674203   \n",
       "max_iou_mean                                                             0.912811   \n",
       "max_iou_over_time               [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
       "max_extrude_count                                                              20   \n",
       "\n",
       "                                                                              gin  \\\n",
       "log_count                                                                    1708   \n",
       "files_processed                                                              1725   \n",
       "files_success                                                                1698   \n",
       "files_fail                                                                     17   \n",
       "files_crash                                                                   NaN   \n",
       "files_timeout                                                                  10   \n",
       "test_file_count                                                              1725   \n",
       "time_taken_mean                                                         21.478924   \n",
       "budget_used_mean                                                        37.750725   \n",
       "conciseness                     {'41026_295d1dc8_0003': 2.0, '41234_74275eb0_0...   \n",
       "conciseness_mean                                                         1.004153   \n",
       "perfect_reconstruction                                                       1087   \n",
       "perfect_reconstructions         [0, 2, 88, 12, 0, 1, 1, 1, 0, 4, 21, 0, 0, 0, ...   \n",
       "perfect_reconstruction_files    {'41026_295d1dc8_0003': 2, '41234_74275eb0_000...   \n",
       "perfect_reconstruction_percent                                           0.630145   \n",
       "max_iou_mean                                                             0.876091   \n",
       "max_iou_over_time               [[0.5257213071535312, 0.5257213071535312, 0.52...   \n",
       "max_extrude_count                                                              20   \n",
       "\n",
       "                                                                             rand  \n",
       "log_count                                                                    1707  \n",
       "files_processed                                                              1725  \n",
       "files_success                                                                1693  \n",
       "files_fail                                                                     17  \n",
       "files_crash                                                                    14  \n",
       "files_timeout                                                                   1  \n",
       "test_file_count                                                              1725  \n",
       "time_taken_mean                                                          4.960296  \n",
       "budget_used_mean                                                        51.027826  \n",
       "conciseness                     {'41026_295d1dc8_0003': 2.0, '41234_74275eb0_0...  \n",
       "conciseness_mean                                                         1.282436  \n",
       "perfect_reconstruction                                                        928  \n",
       "perfect_reconstructions         [0, 8, 100, 0, 0, 75, 15, 1, 0, 0, 0, 0, 0, 0,...  \n",
       "perfect_reconstruction_files    {'41026_295d1dc8_0003': 8, '41234_74275eb0_000...  \n",
       "perfect_reconstruction_percent                                           0.537971  \n",
       "max_iou_mean                                                             0.838626  \n",
       "max_iou_over_time               [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...  \n",
       "max_extrude_count                                                              20  "
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>gcn</th>\n      <th>mlp</th>\n      <th>gat</th>\n      <th>gin</th>\n      <th>rand</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>log_count</th>\n      <td>1697</td>\n      <td>1692</td>\n      <td>1708</td>\n      <td>1708</td>\n      <td>1707</td>\n    </tr>\n    <tr>\n      <th>files_processed</th>\n      <td>1725</td>\n      <td>1725</td>\n      <td>1725</td>\n      <td>1725</td>\n      <td>1725</td>\n    </tr>\n    <tr>\n      <th>files_success</th>\n      <td>1688</td>\n      <td>1687</td>\n      <td>1702</td>\n      <td>1698</td>\n      <td>1693</td>\n    </tr>\n    <tr>\n      <th>files_fail</th>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n    </tr>\n    <tr>\n      <th>files_crash</th>\n      <td>15</td>\n      <td>17</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>14</td>\n    </tr>\n    <tr>\n      <th>files_timeout</th>\n      <td>5</td>\n      <td>4</td>\n      <td>6</td>\n      <td>10</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>test_file_count</th>\n      <td>1725</td>\n      <td>1725</td>\n      <td>1725</td>\n      <td>1725</td>\n      <td>1725</td>\n    </tr>\n    <tr>\n      <th>time_taken_mean</th>\n      <td>19.486288</td>\n      <td>19.06079</td>\n      <td>23.172136</td>\n      <td>21.478924</td>\n      <td>4.960296</td>\n    </tr>\n    <tr>\n      <th>budget_used_mean</th>\n      <td>33.373913</td>\n      <td>39.648696</td>\n      <td>34.371014</td>\n      <td>37.750725</td>\n      <td>51.027826</td>\n    </tr>\n    <tr>\n      <th>conciseness</th>\n      <td>{'41026_295d1dc8_0003': 1.0, '41234_74275eb0_0...</td>\n      <td>{'41026_295d1dc8_0003': 1.0, '41234_74275eb0_0...</td>\n      <td>{'41026_295d1dc8_0003': 1.0, '41234_74275eb0_0...</td>\n      <td>{'41026_295d1dc8_0003': 2.0, '41234_74275eb0_0...</td>\n      <td>{'41026_295d1dc8_0003': 2.0, '41234_74275eb0_0...</td>\n    </tr>\n    <tr>\n      <th>conciseness_mean</th>\n      <td>1.0168</td>\n      <td>0.976339</td>\n      <td>1.020557</td>\n      <td>1.004153</td>\n      <td>1.282436</td>\n    </tr>\n    <tr>\n      <th>perfect_reconstruction</th>\n      <td>1165</td>\n      <td>1029</td>\n      <td>1163</td>\n      <td>1087</td>\n      <td>928</td>\n    </tr>\n    <tr>\n      <th>perfect_reconstructions</th>\n      <td>[0, 1, 56, 0, 0, 1, 1, 1, 0, 2, 2, 0, 0, 0, 1,...</td>\n      <td>[0, 1, 10, 0, 0, 1, 1, 1, 0, 2, 0, 0, 0, 0, 1,...</td>\n      <td>[0, 1, 2, 37, 0, 1, 1, 1, 0, 0, 0, 0, 0, 24, 1...</td>\n      <td>[0, 2, 88, 12, 0, 1, 1, 1, 0, 4, 21, 0, 0, 0, ...</td>\n      <td>[0, 8, 100, 0, 0, 75, 15, 1, 0, 0, 0, 0, 0, 0,...</td>\n    </tr>\n    <tr>\n      <th>perfect_reconstruction_files</th>\n      <td>{'41026_295d1dc8_0003': 1, '41234_74275eb0_000...</td>\n      <td>{'41026_295d1dc8_0003': 1, '41234_74275eb0_000...</td>\n      <td>{'41026_295d1dc8_0003': 1, '41234_74275eb0_000...</td>\n      <td>{'41026_295d1dc8_0003': 2, '41234_74275eb0_000...</td>\n      <td>{'41026_295d1dc8_0003': 8, '41234_74275eb0_000...</td>\n    </tr>\n    <tr>\n      <th>perfect_reconstruction_percent</th>\n      <td>0.675362</td>\n      <td>0.596522</td>\n      <td>0.674203</td>\n      <td>0.630145</td>\n      <td>0.537971</td>\n    </tr>\n    <tr>\n      <th>max_iou_mean</th>\n      <td>0.904203</td>\n      <td>0.85961</td>\n      <td>0.912811</td>\n      <td>0.876091</td>\n      <td>0.838626</td>\n    </tr>\n    <tr>\n      <th>max_iou_over_time</th>\n      <td>[[0.8846035521300826, 0.8846035521300826, 0.88...</td>\n      <td>[[0.0, 0.8846035521300826, 0.8846035521300826,...</td>\n      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n      <td>[[0.5257213071535312, 0.5257213071535312, 0.52...</td>\n      <td>[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n    </tr>\n    <tr>\n      <th>max_extrude_count</th>\n      <td>20</td>\n      <td>20</td>\n      <td>20</td>\n      <td>20</td>\n      <td>20</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 22
    }
   ],
   "source": [
    "# # Beam Search\n",
    "# search_label_beam = \"Beam\"\n",
    "# log_dirs_beam = [\n",
    "#     Path(\"../log/super_aug_beam_01\"), \n",
    "#     Path(\"../log/super_beam_07\"), \n",
    "#     Path(\"../log/super_nogcn_beam_01\"),\n",
    "#     Path(\"../log/rand_beam_01\")\n",
    "# ]\n",
    "# # Best Search\n",
    "# search_label_best = \"Best\"\n",
    "# log_dirs_best = [\n",
    "#     Path(\"../log/super_aug_best_01\"), \n",
    "#     Path(\"../log/super_best_04\"), \n",
    "#     Path(\"../log/super_nogcn_best_01\"), \n",
    "#     Path(\"../log/rand_best_01\")    \n",
    "# ]\n",
    "#\n",
    "# -------------------\n",
    "#\n",
    "# # Random Search\n",
    "# search_label_rand = \"Random\"\n",
    "# log_dirs_rand = [\n",
    "#     Path(\"../log/super_aug_rand_01\"),\n",
    "#     Path(\"../log/super_rand_08\"),\n",
    "#     Path(\"../log/super_nogcn_rand_02\"),     \n",
    "#     Path(\"../log/rand_rand_03\")\n",
    "# ]\n",
    "# agent_labels = [\n",
    "#     \"mpn-aug\",\n",
    "#     \"mpn\",   \n",
    "#     \"mlp\",\n",
    "#     \"rand\"\n",
    "# ]\n",
    "# search_label = search_label_rand\n",
    "# log_dirs = log_dirs_rand\n",
    "# labels = agent_labels\n",
    "#\n",
    "# -------------------\n",
    "#\n",
    "# # Search Comparison\n",
    "# search_label_rand = \"gcn\"\n",
    "# log_dirs_search = [\n",
    "#     Path(\"../log/super_beam_07\"),\n",
    "#     Path(\"../log/super_best_04\"),\n",
    "#     Path(\"../log/super_rand_08\")\n",
    "# ]\n",
    "# search_labels = [\n",
    "#     \"beam\",\n",
    "#     \"best\",   \n",
    "#     \"rand\"\n",
    "# ]\n",
    "# search_label = search_label_rand\n",
    "# log_dirs = log_dirs_search\n",
    "# labels = search_labels\n",
    "#\n",
    "# -------------------\n",
    "#\n",
    "# # Synthetic Data Comparison\n",
    "# search_label_rand = \"GCN Agent, Random Search\"\n",
    "# log_dirs_search = [\n",
    "#     Path(\"../log/super_rand_08\"),\n",
    "#     Path(\"../log/super_aug_rand_01\"),        \n",
    "#     Path(\"../log/super_semisyn_rand_01\"),\n",
    "#     Path(\"../log/super_syn_rand_01\")\n",
    "# ]\n",
    "# agent_labels = [\n",
    "#     \"real\",\n",
    "#     \"aug\",\n",
    "#     \"semi-syn\",\n",
    "#     \"syn\"\n",
    "# ]\n",
    "# search_label = search_label_rand\n",
    "# log_dirs = log_dirs_search\n",
    "# labels = agent_labels\n",
    "#\n",
    "# -------------------\n",
    "#\n",
    "# SOTA Baseline Comparison\n",
    "search_label_rand = \"Random\"\n",
    "log_dirs_search = [\n",
    "    Path(\"../log/super_rand_08\"),\n",
    "    Path(\"../log/super_nogcn_rand_02\"),\n",
    "    Path(\"../log/super_gat_rand_01\"),\n",
    "    Path(\"../log/super_gin_rand_01\"),\n",
    "    Path(\"../log/rand_rand_03\")\n",
    "]\n",
    "agent_labels = [\n",
    "    \"gcn\",\n",
    "    \"mlp\",   \n",
    "    \"gat\",\n",
    "    \"gin\",\n",
    "    \"rand\"\n",
    "]\n",
    "search_label = search_label_rand\n",
    "log_dirs = log_dirs_search\n",
    "labels = agent_labels\n",
    "#\n",
    "# -------------------\n",
    "#\n",
    "# # SOTA Baseline Comparison All Searches\n",
    "# log_dirs_search = [  \n",
    "#     Path(\"../log/super_rand_08\"),\n",
    "#     Path(\"../log/super_beam_07\"),\n",
    "#     Path(\"../log/super_best_04\"),\n",
    "#     Path(\"../log/super_nogcn_rand_02\"),\n",
    "#     Path(\"../log/super_nogcn_beam_01\"),\n",
    "#     Path(\"../log/super_nogcn_best_01\"),\n",
    "#     Path(\"../log/super_gat_rand_01\"),\n",
    "#     Path(\"../log/super_gat_beam_01\"),\n",
    "#     Path(\"../log/super_gat_best_01\"),\n",
    "#     Path(\"../log/super_gin_rand_01\"),\n",
    "#     Path(\"../log/super_gin_beam_01\"),\n",
    "#     Path(\"../log/super_gin_best_01\"),\n",
    "#     Path(\"../log/rand_rand_03\"),\n",
    "#     Path(\"../log/rand_beam_01\"),\n",
    "#     Path(\"../log/rand_best_01\")\n",
    "# ]\n",
    "# agent_labels = [\n",
    "#     \"gcn-rand\",\n",
    "#     \"gcn-beam\",\n",
    "#     \"gcn-best\",   \n",
    "#     \"mlp-rand\",   \n",
    "#     \"mlp-beam\",   \n",
    "#     \"mlp-best\",   \n",
    "#     \"gat-rand\",\n",
    "#     \"gat-beam\",\n",
    "#     \"gat-best\",\n",
    "#     \"gin-rand\",\n",
    "#     \"gin-beam\",\n",
    "#     \"gin-best\",\n",
    "#     \"rand-rand\",\n",
    "#     \"rand-beam\",\n",
    "#     \"rand-best\"\n",
    "# ]\n",
    "# search_label = \"All Searches\"\n",
    "# log_dirs = log_dirs_search\n",
    "# labels = agent_labels\n",
    "\n",
    "colors = sns.color_palette(\"hls\", len(labels))\n",
    "results = {}\n",
    "qual_results = {}\n",
    "for log_dir, label in zip(log_dirs, labels):\n",
    "    result, qual_result = evaluate(log_dir, dataset_dir, split_file)\n",
    "    results[label] = result\n",
    "    qual_results[label] = qual_result\n",
    "\n",
    "results_df = pd.DataFrame(results)\n",
    "test_file_count = results_df.loc[\"test_file_count\"][0]\n",
    "results_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the reconstruction performance over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "max_iou_mean_over_time = [ np.mean(result, axis=0) for result in results_df.loc[\"max_iou_over_time\"].tolist() ]\n",
    "plot_iou_over_time(max_iou_mean_over_time, labels, f\"Average Reconstruction IoU - {search_label}\", color=colors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cumulative Exact Reconstructions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "perfect_reconstructions = results_df.loc[\"perfect_reconstructions\"].tolist()\n",
    "\n",
    "full_recon_ys = [None] * len(perfect_reconstructions)\n",
    "full_recon_xs = [None] * len(perfect_reconstructions)\n",
    "\n",
    "for index, perfect_reconstruction in enumerate(perfect_reconstructions):\n",
    "    (unique, counts) = np.unique(perfect_reconstruction, return_counts=True)\n",
    "    # Zero out the reconstructions that failed\n",
    "    counts[0] = 0\n",
    "    cumsum = np.cumsum(counts) / test_file_count\n",
    "    full_recon_ys[index] = cumsum\n",
    "    full_recon_xs[index] = unique\n",
    "\n",
    "extrude_counts = get_design_extrude_counts(dataset_dir, split_file)\n",
    "(unique, counts) = np.unique(extrude_counts, return_counts=True)\n",
    "cumsum = list(np.cumsum(counts) / test_file_count)\n",
    "unique = list(unique)\n",
    "cumsum.insert(0, 0)\n",
    "unique.insert(0, 0)\n",
    "full_recon_ys.append(cumsum)\n",
    "full_recon_xs.append(unique)\n",
    "labels_gt = copy.deepcopy(labels)\n",
    "labels_gt.append(\"Ground Truth\")\n",
    "colors_gt = copy.deepcopy(colors)\n",
    "colors_gt.append([0.0, 0.0, 0.0, 0.3])\n",
    "regraph_limit_percent = 0.8\n",
    "\n",
    "plot_perfect_reconstructions(full_recon_xs, full_recon_ys, labels_gt, f\"Cumulative Exact Reconstructions - {search_label}\", regraph_limit=regraph_limit_percent, color=colors_gt)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IoU Average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "max_iou_mean_100 = []\n",
    "max_iou_mean_20 = []\n",
    "for max_iou_result in max_iou_mean_over_time:\n",
    "    max_iou_mean_100.append(np.max(max_iou_result[:100]))\n",
    "    max_iou_mean_20.append(np.max(max_iou_result[:20]))\n",
    "\n",
    "# Plot the results after 100 steps\n",
    "max_iou_mean = max_iou_mean_100\n",
    "\n",
    "max_iou_mean_data = zip(max_iou_mean, labels, colors)\n",
    "max_iou_mean_data_sorted = sorted(max_iou_mean_data, reverse=True)\n",
    "max_iou_mean_data_unzip = list(zip(*max_iou_mean_data_sorted))\n",
    "\n",
    "plot_bargraph(max_iou_mean_data_unzip[1], max_iou_mean_data_unzip[0], \"IoU Average\", color=max_iou_mean_data_unzip[2], ylim=[0,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Average Time Taken"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "time_taken_mean = results_df.loc[\"time_taken_mean\"].tolist()\n",
    "\n",
    "time_taken_mean_data = zip(time_taken_mean, labels, colors)\n",
    "time_taken_mean_data_sorted = sorted(time_taken_mean_data, reverse=True)\n",
    "time_taken_mean_data_unzip = list(zip(*time_taken_mean_data_sorted))\n",
    "\n",
    "font_family = \"Georgia\"\n",
    "title_font = font_manager.FontProperties(family=font_family,\n",
    "                                   weight='normal',\n",
    "                                   style='normal', size=20)\n",
    "legend_font = font_manager.FontProperties(family=font_family,\n",
    "                                   weight='normal',\n",
    "                                   style='normal', size=12)\n",
    "label_font = font_manager.FontProperties(family=font_family,\n",
    "                                   weight='normal',\n",
    "                                   style='italic', size=18)\n",
    "graph_color = \"deepskyblue\"\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(19, 5))\n",
    "\n",
    "ax.bar(time_taken_mean_data_unzip[1], time_taken_mean_data_unzip[0], color=graph_color)\n",
    "ax.set_title(\"Average Reconstruction Time Per Design\", fontproperties=title_font)\n",
    "ax.set_xlabel(\"Agent-Search\", fontproperties=label_font)\n",
    "ax.set_ylabel(\"Seconds\", fontproperties=label_font)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "for tick in ax.get_xticklabels():\n",
    "    tick.set_fontname(font_family)\n",
    "for tick in ax.get_yticklabels():\n",
    "    tick.set_fontname(font_family)\n",
    "ax.tick_params(axis='both', labelsize=14)\n",
    "ax.yaxis.grid(linestyle='--')\n",
    "ax.set_axisbelow(True)\n",
    "results_plot_file = Path(\"../log/average_reconstruction_time.pdf\")\n",
    "# Save and include the legend\n",
    "# plt.savefig(results_plot_file, bbox_inches='tight')  \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conciseness Average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "conciseness_mean = results_df.loc[\"conciseness_mean\"].tolist()\n",
    "\n",
    "conciseness_mean_data = zip(conciseness_mean, labels, colors)\n",
    "conciseness_mean_data_sorted = sorted(conciseness_mean_data, reverse=False)\n",
    "conciseness_mean_data_unzip = list(zip(*conciseness_mean_data_sorted))\n",
    "\n",
    "plot_bargraph(conciseness_mean_data_unzip[1], conciseness_mean_data_unzip[0], \"Conciseness\", color=conciseness_mean_data_unzip[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "  Agent      IoU           Exact Reconstruction %           Conciseness\n",
       "        20 Steps 100 Steps               20 Steps 100 Steps            \n",
       "0   gcn   0.8644    0.9042                 0.6232    0.6754      1.0168\n",
       "1   mlp   0.8274    0.8596                 0.5658    0.5965      0.9763\n",
       "2   gat   0.8742    0.9128                 0.6191    0.6742      1.0206\n",
       "3   gin   0.8346    0.8761                 0.5901    0.6301      1.0042\n",
       "4  rand   0.6840    0.8386                 0.4157    0.5380      1.2824"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead tr th {\n        text-align: left;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th>Agent</th>\n      <th colspan=\"2\" halign=\"left\">IoU</th>\n      <th colspan=\"2\" halign=\"left\">Exact Reconstruction %</th>\n      <th>Conciseness</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th></th>\n      <th>20 Steps</th>\n      <th>100 Steps</th>\n      <th>20 Steps</th>\n      <th>100 Steps</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>gcn</td>\n      <td>0.8644</td>\n      <td>0.9042</td>\n      <td>0.6232</td>\n      <td>0.6754</td>\n      <td>1.0168</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>mlp</td>\n      <td>0.8274</td>\n      <td>0.8596</td>\n      <td>0.5658</td>\n      <td>0.5965</td>\n      <td>0.9763</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>gat</td>\n      <td>0.8742</td>\n      <td>0.9128</td>\n      <td>0.6191</td>\n      <td>0.6742</td>\n      <td>1.0206</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>gin</td>\n      <td>0.8346</td>\n      <td>0.8761</td>\n      <td>0.5901</td>\n      <td>0.6301</td>\n      <td>1.0042</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>rand</td>\n      <td>0.6840</td>\n      <td>0.8386</td>\n      <td>0.4157</td>\n      <td>0.5380</td>\n      <td>1.2824</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 28
    }
   ],
   "source": [
    "# Get the number of full reconstructions at 20 and 100 steps\n",
    "full_recon_20 = []\n",
    "full_recon_100 = []\n",
    "for xs, ys in zip(full_recon_xs[:-1], full_recon_ys[:-1]):\n",
    "    prev_x = 0\n",
    "    for index, x in enumerate(xs):\n",
    "        if x == 20:\n",
    "            result_y = ys[index]\n",
    "            full_recon_20.append(result_y)\n",
    "            break\n",
    "        elif x > 20:\n",
    "            result_y = ys[prev_index]\n",
    "            full_recon_20.append(result_y)\n",
    "            break\n",
    "        prev_index = index\n",
    "    full_recon_100.append(ys[-1])        \n",
    "\n",
    "columns = pd.MultiIndex.from_product([['IoU', 'Exact Reconstruction %'], ['20 Steps', '100 Steps']])\n",
    "table_results = np.transpose([\n",
    "    max_iou_mean_20,\n",
    "    max_iou_mean_100,    \n",
    "    full_recon_20,\n",
    "    full_recon_100\n",
    "])\n",
    "\n",
    "agents = []\n",
    "searchs = []\n",
    "data_aug = []\n",
    "for label in labels:\n",
    "    label_parts = label.split(\"-\")\n",
    "    if len(label_parts) > 1:\n",
    "        agents.append(label_parts[0])\n",
    "        searchs.append(label_parts[1])\n",
    "    else:\n",
    "        agents.append(label)\n",
    "\n",
    "df = pd.DataFrame(table_results, columns=columns)\n",
    "df[\"Conciseness\"] = conciseness_mean\n",
    "df[\"Agent\"] = agents\n",
    "# df[\"Data Aug\"] = data_aug\n",
    "# df[\"Search\"] = searchs\n",
    "cols = df.columns.tolist()\n",
    "cols_updated = cols[-1:]\n",
    "cols_updated.extend(cols[:-1])\n",
    "df = df[cols_updated]\n",
    "df.round(4).to_clipboard()\n",
    "df.round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def round(x):\n",
    "    return f\"{x:.4f}\"\n",
    "\n",
    "def percent(x):\n",
    "    return f\"{x * 100:.2f}%\"\n",
    "\n",
    "results_table_file = Path(\"../log/reconstruction_results.tex\")\n",
    "df.to_latex(buf=results_table_file, index=False, formatters=[None, round, round, round, round, round], multicolumn=True, column_format=\"l|rrrrrr\", multicolumn_format=\"c\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results Figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "font_family = \"Georgia\"\n",
    "title_font = font_manager.FontProperties(family=font_family,\n",
    "                                   weight='normal',\n",
    "                                   style='normal', size=19)\n",
    "legend_font = font_manager.FontProperties(family=font_family,\n",
    "                                   weight='normal',\n",
    "                                   style='normal', size=16)\n",
    "label_font = font_manager.FontProperties(family=font_family,\n",
    "                                   weight='normal',\n",
    "                                   style='italic', size=18)\n",
    "line_width = 3\n",
    "linestyle = \"-\"\n",
    "\n",
    "fig, ax1 = plt.subplots(1, 1)\n",
    "fig.set_size_inches(10, 5.25)\n",
    "\n",
    "# Baseline Experiment\n",
    "sort_order = [3, 1, 4, 2, 0, 5]\n",
    "# gcn, mlp, gat, gin, rand, gt\n",
    "recon_iou_data = zip(max_iou_mean_over_time, labels_gt, colors_gt, sort_order)\n",
    "recon_iou_data = sorted(recon_iou_data, key = lambda x: x[3], reverse=True)\n",
    "\n",
    "# ax1.set_ylim([-0.05, 1.05])\n",
    "ax1.set_ylim([0.3, 1.03])\n",
    "for ys, label, color, order in recon_iou_data:\n",
    "    ax1.plot(range(1, len(ys) + 1), ys, label=label, color=color, linewidth=line_width, linestyle=linestyle)\n",
    "ax1.set_xlabel('Search Step', fontproperties=label_font)\n",
    "ax1.set_ylabel('IoU', fontproperties=label_font)\n",
    "ax1.set_title('Reconstruction IoU', fontproperties=title_font)\n",
    "for tick in ax1.get_xticklabels():\n",
    "    tick.set_fontname(font_family)\n",
    "for tick in ax1.get_yticklabels():\n",
    "    tick.set_fontname(font_family)\n",
    "ax1.tick_params(axis='both', labelsize=14)\n",
    "ax1.yaxis.grid(linestyle='--')\n",
    "plt.legend(loc=\"lower right\", prop=legend_font, frameon=False, bbox_transform=fig.transFigure, bbox_to_anchor=(0.87,-0.1), ncol=5)\n",
    "\n",
    "results_plot_file = Path(\"../log/reconstruction_results_iou.pdf\")\n",
    "# Save and include the legend\n",
    "# plt.savefig(results_plot_file, bbox_extra_artists=(lgd,), bbox_inches='tight')  \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(1, 1)\n",
    "fig.set_size_inches(10, 5.25)\n",
    "\n",
    "# # ax2.set_ylim([-0.05, 1.05])\n",
    "ax1.set_ylim([0.3, 1.03])\n",
    "# Custom order for the legend\n",
    "# Search comparison\n",
    "# sort_order = [0, 2, 1, 3]\n",
    "# Augmentation Experiment\n",
    "# sort_order = [3, 2, 1, 0, 4]\n",
    "# Baseline Experiment\n",
    "sort_order = [3, 1, 4, 2, 0, 5]\n",
    "# gcn, mlp, gat, gin, rand, gt\n",
    "full_recon_data = zip(full_recon_xs, full_recon_ys, labels_gt, colors_gt, sort_order)\n",
    "full_recon_data = sorted(full_recon_data, key = lambda x: x[4], reverse=True)\n",
    "                                \n",
    "for xs, ys, label, color, order in full_recon_data:\n",
    "    linestyle = \"-\"\n",
    "    if label == \"Ground Truth\":\n",
    "        linestyle = \"--\"\n",
    "    ax1.plot(xs, ys, label=label, color=color, linewidth=line_width, linestyle=linestyle)\n",
    "ax1.set_xlabel('Search Step', fontproperties=label_font)\n",
    "ax1.set_ylabel('Exact Reconstruction %', fontproperties=label_font)\n",
    "lgd = plt.legend(loc=\"lower right\", prop=legend_font, frameon=False, bbox_transform=fig.transFigure, bbox_to_anchor=(0.91,-0.1), ncol=6, handletextpad=0.5, columnspacing=1.25)\n",
    "ax1.set_title(\"Cumulative Exact Reconstructions\", fontproperties=title_font)\n",
    "for tick in ax1.get_xticklabels():\n",
    "    tick.set_fontname(font_family)\n",
    "for tick in ax1.get_yticklabels():\n",
    "    tick.set_fontname(font_family)\n",
    "ax1.tick_params(axis='both', labelsize=14)\n",
    "ax1.yaxis.grid(linestyle='--')\n",
    "ax1.axhline(y=regraph_limit_percent, color='dimgray', linestyle='--')\n",
    "ax1.text(48, regraph_limit_percent + 0.02,'Estimated Representation Upper Limit', fontproperties=legend_font)\n",
    "\n",
    "results_plot_file = Path(\"../log/reconstruction_results_exact.pdf\")\n",
    "# Save and include the legend\n",
    "plt.savefig(results_plot_file, bbox_extra_artists=(lgd,), bbox_inches='tight')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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